```yaml
product: AlterLab
title: Crexi Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-08-08
canonical_facts:
  - "Build a reliable real-estate data pipeline using a crexi data api approach. Learn to extract structured JSON for pricing, addresses, and property specs."
source_url: https://alterlab.io/blog/crexi-data-api-extract-structured-json-in-2026
```

*Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.*

## TL;DR
To get structured Crexi data via API, use the AlterLab Extract API to send a target URL and a JSON schema definition. The API handles proxy rotation and anti-bot bypass, returning validated, typed JSON containing specific fields like price, address, and property specs without requiring manual HTML parsing.

## Why use Crexi data?
Commercial and residential real-estate data is a primary signal for several high-value engineering use cases:

1. **AI Training and RAG**: Feeding real-time listing data into Large Language Models (LLMs) to build real-estate advisory bots or automated valuation models.
2. **Market Analytics**: Tracking price-per-square-foot trends across specific zip codes to identify undervalued assets.
3. **Competitive Intelligence**: Monitoring new listings and price drops in real-time to trigger automated alerts for investment teams.

## What data can you extract?
When building a data pipeline for Crexi, you should focus on publicly available listing attributes. By defining a strict schema, you can ensure your database receives consistent types.

Common extractable fields include:
- **Property Address**: Full string including city, state, and zip.
- **Listing Price**: Numerical value or string (e.g., "$1,200,000").
- **Property Specs**: Square footage (sqft), bedroom count, and bathroom count.
- **Listing Date**: The timestamp of when the property was posted.
- **Property Type**: Classification (e.g., Industrial, Retail, Multi-family).

## The extraction approach
Most developers start by attempting raw HTTP requests or using headless browsers like Playwright. This approach is fragile for three reasons:
1. **Dynamic Content**: Modern real-estate portals use heavy JavaScript rendering that simple GET requests cannot capture.
2. **Bot Detection**: Sophisticated anti-bot systems flag non-browser fingerprints and data-center IP ranges.
3. **DOM Volatility**: CSS selectors change frequently. A small update to the site's frontend breaks your entire regex or BeautifulSoup logic.

Moving to a data API shifts the burden of maintenance from your team to the infrastructure. Instead of managing a fleet of proxies and updating selectors, you define the *shape* of the data you want.

1. **Define Schema** — 
2. **Call Extract API** — 
3. **Receive Typed JSON** — 

## Quick start with AlterLab Extract API
To begin, follow the [Getting started guide](/docs/quickstart/installation) to configure your environment. The Extract API allows you to pass a URL and a JSON schema; the engine then uses AI to locate the data and validate it against your types.

Refer to the [Extract API docs](/docs/api/extract) for a full list of parameters.

### Python Implementation
The following example demonstrates how to extract core property details from a specific Crexi listing.

```python title="extract_crexi-com.py" {5-12}
import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "address": {
      "type": "string",
      "description": "The address field"
    },
    "price": {
      "type": "string",
      "description": "The price field"
    },
    "bedrooms": {
      "type": "string",
      "description": "The bedrooms field"
    },
    "bathrooms": {
      "type": "string",
      "description": "The bathrooms field"
    },
    "sqft": {
      "type": "string",
      "description": "The sqft field"
    },
    "listing_date": {
      "type": "string",
      "description": "The listing date field"
    }
  }
}

result = client.extract(
    url="https://crexi.com/example-page",
    schema=schema,
)
print(result.data)
```

### cURL Implementation
For lightweight integrations or shell scripts, use the REST endpoint directly.

```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://crexi.com/example-page",
    "schema": {"properties": {"address": {"type": "string"}, "price": {"type": "string"}, "bedrooms": {"type": "string"}}}
  }'
```

<div data-infographic="try-it" data-url="https://crexi.com" data-description="Extract structured real-estate data from Crexi"></div>

## Define your schema
The power of a data API lies in the schema. Rather than hoping the scraper finds the right `div`, you provide a JSON Schema (Draft 7) that acts as a contract.

**Example Structured Output:**
When the API processes the request, it returns a clean JSON object:
```json title="response.json"
{
  "address": "123 Main St, Dallas, TX 75201",
  "price": "$2,500,000",
  "bedrooms": "0",
  "bathrooms": "2",
  "sqft": "12,000",
  "listing_date": "2026-01-15"
}
```
If the AI cannot find a field, it returns `null` rather than guessing, ensuring your downstream pipeline doesn't ingest "hallucinated" data.

## Handle pagination and scale
When scaling from a single page to thousands of listings, synchronous requests become a bottleneck. For high-volume Crexi data extraction, use asynchronous batch jobs.

### Async Batch Example
Instead of waiting for each response, dispatch multiple URLs and poll for the results.

```python title="batch_extract.py" {8-15}
import alterlab

client = alterlab.Client("YOUR_API_KEY")

urls = ["https://crexi.com/listing-1", "https://crexi.com/listing-2", "https://crexi.com/listing-3"]
schema = {"properties": {"price": {"type": "string"}, "address": {"type": "string"}}}

# Dispatch async jobs
job_ids = []
for url in urls:
    job = client.extract_async(url=url, schema=schema)
    job_ids.append(job.id)

# Poll for results
for j_id in job_ids:
    res = client.get_job_result(j_id)
    print(f"Result for {j_id}: {res.data}")
```

### Cost and Optimization
To manage spend at scale, use the cost estimation endpoint before committing to a large batch. This is critical when using complex schemas that require more LLM orchestration.

Costs are based on a pay-as-you-go balance. You can review the [AlterLab pricing](/pricing) page to see how balance is deducted per request.

- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output

## Key takeaways
- **Avoid raw parsing**: HTML structures on real-estate sites change too often.
- **Use Schemas**: Define your data requirements in JSON Schema to ensure type safety.
- **Scale Asynchronously**: Use `extract_async` for large datasets to avoid timeout issues.
- **Stay Compliant**: Always target public data and respect `robots.txt`.

AlterLab // Web Data, Simplified.

## Frequently Asked Questions

### Is there an official Crexi data API?

Crexi does not provide a public, self-service API for general data extraction. AlterLab fills this gap by transforming public web pages into a structured JSON API.

### What Crexi data can I extract with AlterLab?

You can extract any publicly available listing data, including property addresses, pricing, square footage, and bedroom/bathroom counts, delivered via a typed JSON schema.

### How much does Crexi data extraction cost?

AlterLab uses a pay-as-you-go model where you pay only for the data you extract. Check our pricing page for current rates and balance options.

## Related

- [Rate My Professors Data API: Extract Structured JSON in 2026](<https://alterlab.io/blog/rate-my-professors-data-api-extract-structured-json-in-2026>)
- [How to Scrape Shopee Data: Complete Guide for 2026](<https://alterlab.io/blog/how-to-scrape-shopee-data-complete-guide-for-2026>)
- [Hardening Worker Retries and Refund Fallbacks in AlterLab](<https://alterlab.io/blog/hardening-worker-retries-and-refund-fallbacks-in-alterlab>)